Para Rowing Virtual Classification
Bibliographic record
Abstract
OBJECTIVE: Athlete assessment for para rowing classification has initiated use of virtual assessments over teleconferencing. This study explores the experiences of classifiers, athletes, and national rowing federations during virtual classification and provides recommendations for potential future use. DESIGN: Participants in virtual classification assessments were invited to complete one of two online surveys specific to their roles. Surveys contained a mix of open- and closed-ended questions, with 5-point Likert scale questions exploring agreement. RESULTS: Twenty-eight participants responded. Although 96% (27/28) of participants agreed that in-person classification remains the gold standard for classification before competing in World Rowing regattas, 82% (23/28) agreed that virtual classification is a viable option to inform initial athlete classification. Results suggest a need for standardized processes addressing use of communication technology, preparation of in-person assessor, respectful environment for athletes, and managing potential risk of error. CONCLUSIONS: Further exploration of use of virtual classification processes is warranted to establish best practices. In-person classification remains a requirement of para rowing participation; however, virtual assessment addresses cost savings and initial classification needs of national rowing federations without trained para rowing classifiers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".